Unknown Heterogeneity, the EC-EM Algorithm, and Large T Approximation.

We study a panel structure with n subjects/entities being observed over T periods. We consider a class of models for each subject's data generating precess, and allow the unknown heterogeneity. In other words, we do not know many types we have, what the types are, and which subjects belong to each type. We propose a large T approximation to the posterior mode on the unknows through the Estimation/Classification (EC) algorithm of El-Gamal and Grether (1995) which is linear in n,T, and the unknown number of types.